Molecular Engineering of Synthetic Motile Systems towards Biological Environments

The goal of this ERC is to develop synthetic motile systems with cilia-like and flagella-like movement based on supramolecular assemblies of controlled shape, size and morphology. With this strategy, we are addressing the great challenge of developing synthetic systems with the ability to move, sense, adapt at the cellular, tissue and systemic level. The systems developed will then allow us to study the effect of propulsive movement on cellular uptake, targeted transport, external guidance and sensing and thus define the active delivery potential of these systems. Assembly from building blocks with pre-programmed functionality able to transfer complexity to their structure and behaviour is the core principle guiding nature and a tool that we have harnessed in our research. Besides their ability to move directionally these complex bio-inspired systems are programmed to sense changes in the environment and consequently to adapt to the changes by regulating their speed, shape and behaviour. Since they are by design catalytically active, they can also change the chemical composition of their environment as well as dynamically regulate the chemical signaling pathways in their interaction with other species. The study of the primary biomimetic complex emergent functions such as motility, adaptivity (regulated and feedback output) and interaction/communication in biological environments will be the goals of this ERC program and will concentrate on 3 work packages built from 5 interconnecting projects. Organic, inorganic catalysts and biocatalysts based on multiple enzymes will be incorporated within asymmetric soft self-assembled structures to generate smart autonomous systems able to harvest different sources of energy from the surrounding environment and generate a feedback response. The final output of the program will be to develop an understanding of the directional movement of engineered synthetic motile systems studied from cellular levels to complex environments.